title: Weighted SNP set analysis in genome-wide association study.
abstract: Genome-wide association studies (GWAS) are popular for identifying genetic variants which are associated with disease risk. Many approaches have been proposed to test multiple single nucleotide polymorphisms (SNPs) in a region simultaneously which considering disadvantages of methods in single locus association analysis. Kernel machine based SNP set analysis is more powerful than single locus analysis, which borrows information from SNPs correlated with causal or tag SNPs. Four types of kernel machine functions and principal component based approach (PCA) were also compared. However, given the loss of power caused by low minor allele frequencies (MAF), we conducted an extension work on PCA and used a new method called weighted PCA (wPCA). Comparative analysis was performed for weighted principal component analysis (wPCA), logistic kernel machine based test (LKM) and principal component analysis (PCA) based on SNP set in the case of different minor allele frequencies (MAF) and linkage disequilibrium (LD) structures. We also applied the three methods to analyze two SNP sets extracted from a real GWAS dataset of non-small cell lung cancer in Han Chinese population. Simulation results show that when the MAF of the causal SNP is low, weighted principal component and weighted IBS are more powerful than PCA and other kernel machine functions at different LD structures and different numbers of causal SNPs. Application of the three methods to a real GWAS dataset indicates that wPCA and wIBS have better performance than the linear kernel, IBS kernel and PCA.
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Dai
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhao
authors.forename: Cheng
authors.initials: C
authors.lastname: Qian
authors.forename: Min
authors.initials: M
authors.lastname: Cai
authors.forename: Ruyang
authors.initials: R
authors.lastname: Zhang
authors.forename: Minjie
authors.initials: M
authors.lastname: Chu
authors.forename: Juncheng
authors.initials: J
authors.lastname: Dai
authors.forename: Zhibin
authors.initials: Z
authors.lastname: Hu
authors.forename: Hongbing
authors.initials: H
authors.lastname: Shen
authors.forename: Feng
authors.initials: F
authors.lastname: Chen
mesh_terms: D044466:Asian People; D002289:Carcinoma, Non-Small-Cell Lung / Q000235:genetics*; D003627:Data Interpretation, Statistical; D005787:Gene Frequency; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D025341:Principal Component Analysis / Q000379:methods